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Merge branch 'comfyanonymous:master' into feature/preview-latent

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Dr.Lt.Data 2023-05-21 09:51:18 +09:00 committed by GitHub
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1 changed files with 44 additions and 49 deletions
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93
comfy/ldm/modules/diffusionmodules/model.py
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@ -146,6 +146,41 @@ class ResnetBlock(nn.Module):
return x+h
def slice_attention(q, k, v):
r1 = torch.zeros_like(k, device=q.device)
scale = (int(q.shape[-1])**(-0.5))
mem_free_total = model_management.get_free_memory(q.device)
gb = 1024 ** 3
tensor_size = q.shape[0] * q.shape[1] * k.shape[2] * q.element_size()
modifier = 3 if q.element_size() == 2 else 2.5
mem_required = tensor_size * modifier
steps = 1
if mem_required > mem_free_total:
steps = 2**(math.ceil(math.log(mem_required / mem_free_total, 2)))
while True:
try:
slice_size = q.shape[1] // steps if (q.shape[1] % steps) == 0 else q.shape[1]
for i in range(0, q.shape[1], slice_size):
end = i + slice_size
s1 = torch.bmm(q[:, i:end], k) * scale
s2 = torch.nn.functional.softmax(s1, dim=2).permute(0,2,1)
del s1
r1[:, :, i:end] = torch.bmm(v, s2)
del s2
break
except model_management.OOM_EXCEPTION as e:
steps *= 2
if steps > 128:
raise e
print("out of memory error, increasing steps and trying again", steps)
return r1
class AttnBlock(nn.Module):
def __init__(self, in_channels):
@ -183,48 +218,15 @@ class AttnBlock(nn.Module):
# compute attention
b,c,h,w = q.shape
scale = (int(c)**(-0.5))
q = q.reshape(b,c,h*w)
q = q.permute(0,2,1) # b,hw,c
k = k.reshape(b,c,h*w) # b,c,hw
v = v.reshape(b,c,h*w)
r1 = torch.zeros_like(k, device=q.device)
mem_free_total = model_management.get_free_memory(q.device)
gb = 1024 ** 3
tensor_size = q.shape[0] * q.shape[1] * k.shape[2] * q.element_size()
modifier = 3 if q.element_size() == 2 else 2.5
mem_required = tensor_size * modifier
steps = 1
if mem_required > mem_free_total:
steps = 2**(math.ceil(math.log(mem_required / mem_free_total, 2)))
while True:
try:
slice_size = q.shape[1] // steps if (q.shape[1] % steps) == 0 else q.shape[1]
for i in range(0, q.shape[1], slice_size):
end = i + slice_size
s1 = torch.bmm(q[:, i:end], k) * scale
s2 = torch.nn.functional.softmax(s1, dim=2).permute(0,2,1)
del s1
r1[:, :, i:end] = torch.bmm(v, s2)
del s2
break
except model_management.OOM_EXCEPTION as e:
steps *= 2
if steps > 128:
raise e
print("out of memory error, increasing steps and trying again", steps)
r1 = slice_attention(q, k, v)
h_ = r1.reshape(b,c,h,w)
del r1
h_ = self.proj_out(h_)
return x+h_
@ -331,25 +333,18 @@ class MemoryEfficientAttnBlockPytorch(nn.Module):
# compute attention
B, C, H, W = q.shape
q, k, v = map(lambda x: rearrange(x, 'b c h w -> b (h w) c'), (q, k, v))
q, k, v = map(
lambda t: t.unsqueeze(3)
.reshape(B, t.shape[1], 1, C)
.permute(0, 2, 1, 3)
.reshape(B * 1, t.shape[1], C)
.contiguous(),
lambda t: t.view(B, 1, C, -1).transpose(2, 3).contiguous(),
(q, k, v),
)
out = torch.nn.functional.scaled_dot_product_attention(q, k, v, attn_mask=None, dropout_p=0.0, is_causal=False)
out = (
out.unsqueeze(0)
.reshape(B, 1, out.shape[1], C)
.permute(0, 2, 1, 3)
.reshape(B, out.shape[1], C)
)
out = rearrange(out, 'b (h w) c -> b c h w', b=B, h=H, w=W, c=C)
try:
out = torch.nn.functional.scaled_dot_product_attention(q, k, v, attn_mask=None, dropout_p=0.0, is_causal=False)
out = out.transpose(2, 3).reshape(B, C, H, W)
except model_management.OOM_EXCEPTION as e:
print("scaled_dot_product_attention OOMed: switched to slice attention")
out = slice_attention(q.view(B, -1, C), k.view(B, -1, C).transpose(1, 2), v.view(B, -1, C).transpose(1, 2)).reshape(B, C, H, W)
out = self.proj_out(out)
return x+out